Reward modulated STDP (Legenstein et al. 2008)

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"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
1 . Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4:e1000180 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Gap Junctions:
Simulation Environment: Python; PCSIM;
Model Concept(s): Pattern Recognition; Spatio-temporal Activity Patterns; Reinforcement Learning; STDP; Biofeedback; Reward-modulated STDP;
This directory contains the scripts for computer simulation 1 
(with differential reinforcement) from 

	Legenstein R, Pecevski D, Maass W 2008 A Learning Theory 
    for Reward-Modulated Spike-Timing-Dependent Plasticity with 
    Application to Biofeedback. PLoS Computational Biology 4(10): e1000180, Oct, 2008 
The produced result is figure 1.

To create the figure you need to:

1. The computer simulation is setup to run as an MPI application
   on 16 computing nodes on a cluster, with 2 processes per computing node.
   To set the list of the names of machines you want to use for computing
   edit the file
2. Start mpdboot on the cluster machines. See the mpich2 documentation on how to do this. 
3. Execute:
    This is an executable file, you don't need to run 'python'.
    Wait until the simulation finishes. You can monitor how the simulation progresses 
    in the sim.out file. The script will produce one hdf5 file in the current directory.

4. Then, to create figure 1 run:
      ipython -pylab


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